For most of the past twenty years, getting found online meant one thing: ranking on Google’s first page. Build the right pages, earn links from credible sites, write content that matched what people were searching for, and the traffic would follow. That model still works. But it is no longer the whole picture. AI-powered search has introduced a new layer that most businesses have not fully accounted for yet. Google now answers many queries directly in the results page before a user even clicks a link. ChatGPT and Perplexity are being used as alternatives to traditional search for an increasing number of queries. And the signals that determine whether your business gets mentioned, cited, or surfaced in these AI responses are meaningfully different from the signals that determined your Google ranking five years ago. This is not a reason to panic or abandon what has worked. It is a reason to think more carefully about what SEO strategy actually means in 2025 and beyond.
What AI Search Actually Does Differently
Traditional search works on a ranking model. You type a query, Google returns a list of results ordered by relevance and authority, and you click through to the one that looks most useful. AI search works on a synthesis model. You ask a question, and the AI reads across multiple sources and constructs an answer. It might cite one of those sources. It might not cite any. The user gets what feels like a direct answer rather than a list of options to evaluate. This matters for businesses because the game has changed from “appear in the list” to “be the source the AI trusts enough to draw from or recommend.” That distinction has significant implications for how content is written, structured, and positioned.
The Signals That Drive AI Visibility
Getting cited or surfaced in AI-generated responses depends on a combination of factors, some of which overlap with traditional SEO and some of which are newer.
| Signal | Traditional SEO Impact | AI Search Impact |
| Domain authority | High | High |
| Content depth and accuracy | Medium | Very high |
| Structured data / schema | Medium | High |
| Brand mentions across the web | Medium | High |
| Named entity recognition | Low | High |
| Answer-format content | Low | Very high |
| Backlink volume | High | Medium |
| Page speed | High | Low |
The most notable shift is that content depth and accuracy have become more important relative to raw link volume. AI models are trained to favour sources that answer questions comprehensively and accurately. A page that thoroughly covers a topic in clear, well-organised prose is more likely to be drawn from than a page that is optimised around keyword density but thin on substance. Structured data has also become more relevant. When your pages include schema markup that tells machines what your business does, where it operates, and what information it holds, AI systems can parse and reference that information more reliably.
What This Means for Content Strategy
The way most businesses write content has historically been shaped by keyword targeting. Pick a term, write a page around it, optimise the metadata. That approach still produces results in traditional search, but it tends to produce content that AI systems find difficult to use. Content that performs well in AI search tends to be written to answer specific questions clearly, to acknowledge complexity where it exists, and to reflect genuine expertise rather than surface-level coverage. A law firm that writes a 300-word page targeting “solicitors in Manchester city centre” is going to be invisible in AI responses compared to one that writes substantive guidance on the legal processes its clients actually need to understand. This does not mean longer is always better. It means more useful is better. The question to ask of any piece of content is: if someone asked this question to an AI, would our page be one of the best possible sources for the answer? If the honest answer is no, that is worth addressing.
The Role of Brand Mentions and Authority
One of the more interesting shifts in AI search is the increased weight placed on brand presence across the wider web. Traditional SEO focused heavily on backlinks, specifically on the number and authority of other sites linking to yours. AI systems are sensitive not just to links but to mentions, to citations without links, to the frequency with which your business or name appears in credible contexts. This is sometimes called entity authority. If your business is mentioned in news articles, listed on professional directories, referenced in forum discussions, or cited in other published content, AI models have more data points from which to establish that your brand is a real, credible entity in your field. For local businesses this is particularly relevant. A business that appears across Google Business Profile, local press, industry directories, and review platforms is going to register more clearly as a known entity than one that exists only on its own website. Something as simple as a consistent business name, address, and phone number across every platform contributes to this.
Where Traditional SEO Still Matters
It is worth being clear that none of this makes conventional search strategy irrelevant. Google still handles the vast majority of search volume. Most purchase decisions, local searches, and B2B research journeys still run through Google. Someone looking for a restaurant near the Northern Quarter, a mechanic near Salford, or a marketing agency based in the city centre is still predominantly searching on Google and clicking through to websites. The businesses that will perform best over the next few years are those that treat traditional SEO and AI search visibility as complementary rather than competing priorities. The technical foundations, the content quality, the backlink profile, the entity authority: all of these feed into both. Working with an SEO team that plans for both matters more now than it did when the goal was simply to rank for a keyword. The strategy needs to account for how search is being used, not just what position a page holds today.
A Practical Checklist for Businesses
If you want to assess where your current online presence sits relative to both traditional and AI search, these are the areas worth reviewing. Content quality: Are your key pages genuinely useful to someone researching your topic, or are they optimised primarily for a keyword? Thin or generic content that exists mainly to target a term tends to underperform in AI-driven results. Structured data: Do your pages use schema markup to identify your business type, location, services, and other key attributes? This makes your content easier for both search engines and AI systems to interpret correctly. Entity consistency: Is your business name, address, and contact information consistent across your website, Google Business Profile, social platforms, and directories? Inconsistencies create ambiguity that can reduce your visibility as a named entity. Brand mentions: Are you being referenced outside your own website? Press coverage, industry directories, guest articles, and review platforms all contribute to the broader signal that your business is credible and established. Answer-format content: Do any of your pages directly answer the questions your customers are asking? FAQ sections, how-to guides, and comparison content tend to perform well when AI systems are synthesising answers.
The Bigger Shift
Search has always evolved. The businesses that adapt to how people are actually finding information tend to outperform those that continue optimising for a version of search that no longer reflects reality. The move toward AI-generated answers is not finished. It is still developing, and neither Google nor the AI platforms have settled on a definitive model for how sources get cited and credited. But the direction is clear enough to start adjusting strategy now, before the gap between businesses that have adapted and those that have not becomes harder to close. The fundamentals still matter. Good content, genuine authority, a technically sound website: none of that has been made irrelevant. What has changed is the additional layer of signals that determine whether a business gets surfaced in an AI response at all. Understanding that layer and building for it is what separates a reactive SEO strategy from one that holds up over time.